Humans can perform various combinations of physical skills without having to relearn skills from scratch every single time. For example, we can swing a bat when walking without having to re-learn such a policy from scratch by composing the individual skills of walking and bat swinging. Enabling robots to combine or compose skills is essential so they can learn novel skills and tasks faster with fewer real world samples. To this end, we propose a novel compositional approach called DSE- Diffusion Score Equilibrium that enables few-shot learning for novel skills by utilizing a combination of base policy priors. Our method is based on probabilistically composing diffusion policies to better model the few-shot demonstration data-distribution than any individual policy. Our goal here is to learn robot motions few-shot and not necessarily goal oriented trajectories. Unfortunately we lack a general purpose metric to evaluate the error between a skill or motion and the provided demonstrations. Hence, we propose a probabilistic measure - Maximum Mean Discrepancy on the Forward Kinematics Kernel (MMD-FK), that is task and action space agnostic. By using our few-shot learning approach DSE, we show that we are able to achieve a reduction of over 30% in MMD-FK across skills and number of demonstrations. Moreover, we show the utility of our approach through real world experiments by teaching novel trajectories to a robot in 5 demonstrations.

翻译：人类能够执行各种物理技能的组合，而无需每次都从头重新学习技能。例如，我们可以在行走时挥动球棒，而无需通过组合行走和挥棒这两个独立技能来从头重新学习此类策略。使机器人具备组合或复合技能的能力至关重要，这样它们就能用更少的真实世界样本更快地学习新技能和任务。为此，我们提出了一种新颖的组合方法，称为DSE-扩散分数均衡，该方法通过利用基础策略先验的组合，实现新技能的少样本学习。我们的方法基于对扩散策略进行概率组合，以比任何单个策略更好地建模少样本演示数据分布。我们的目标是少样本学习机器人运动，而不一定是目标导向的轨迹。遗憾的是，我们缺乏一个通用指标来评估技能或运动与所提供演示之间的误差。因此，我们提出了一种概率度量——基于前向运动学核的最大均值差异（MMD-FK），该度量与任务和动作空间无关。通过使用我们的少样本学习方法DSE，我们表明能够在不同技能和演示数量上实现MMD-FK降低超过30%。此外，我们通过在5次演示中向机器人教授新轨迹的真实世界实验，展示了我们方法的实用性。